Automated handwriting analysis based on pattern recognition: a survey

Samsuryadi, Samsuryadi; Kurniawan, Rudi; Mohamad, Fatma Susilawati

doi:10.11591/ijeecs.v22.i1.pp196-206

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…Literature on personality classification using handwriting analysis shows a concentration on the old personality model. A recent review of automated handwriting analysis conducted by [16] and have mentioned the Five Factor Model, Myers-Briggs [17], Minnesota multiphasic personality inventory [18], and Enneagram [19]. Surprisingly, we have not found any literature that uses the HEXACO personality model.…”

Section: Introductionmentioning

confidence: 68%

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Barata

Akbar²,

Pilliang³

et al. 2022

Int. J. Ind. Optim.

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Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels).

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Section: Introductionmentioning

confidence: 68%

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Barata

Akbar²,

Pilliang³

et al. 2022

Int. J. Ind. Optim.

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show abstract

“…61 − 98.25] 97.11 Finger -Print [15] [75. 35 − 98.60] 90.6 Finger -Vein [16] [79.00 − 100] 96.3 Handwriting [69] [76.00 − 97.00] 87.23 Hand -Geometry [18] [96.23 − 99.81] 98.7 Keystroke [70] [90. 50 − 99.31] 95.1 Lips Motion [20] [53.00 − 100] 90.65 Palm -Print [71] [97.…”

Section: Biometricsmentioning

confidence: 99%

Robustness Assessment of Biometric Authenticators

Dagnas,

Bkakria,

Yaich

2023

2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Biometric authenticators aim to provide a safe, secure, and accurate authentication process in restricted areas. Despite their advantages, biometric authenticators are vulnerable to cyber-attacks, such as spoofing attacks. Spoofing attacks enable malicious actors to masquerade as someone else to gain illegitimate access or privilege. To proceed, the attacker forges fake biometric data or duplicates existing ones. In such a context, the evaluation of the robustness of biometric authenticators is paramount to assessing their resilience potential and derive deployment strategies. Through this work, we propose a generic assessment method, based on a metric which quantifies the robustness of biometrics against cyber-attacks. Our methodology can be adapted to different families of cyber-attacks targeting biometric authentication techniques. We demonstrate our approach by considering spoofing-attacks. To achieve this objective, we present an extended state-of-the-art of biometrics (physiological and behavioural), including emerging biometric technologies. We also provide an overview of spoofing-attacks for each identified biometric mechanism in the literature. Based on this knowledge, we quantify and we combine the characteristics of such attacks into a quantitative robustness metric which can be applied to both a single and a combination of authenticators.

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“…Personality classes are not standard and specific to applications. Samsuryadi and Mohamad [32] made a survey on graphology feature extraction methods and presented the correlation between graphology features and personalities. But the personality traits correlated in this work were non-standard.…”

Section: Related Workmentioning

confidence: 99%

A self adaptive cognitive deep learning framework for classifying graphology features to Big five personality traits

2022

IJATEE

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Graphology is a technique for study and analysis of individual personality from his/her handwriting style. Most of the existing graphology-based solutions for personality detection recognize nonstandard application dependent personalities. Even the very few big five personality recognition approaches have limited accuracy and lacks adaptivity to new handwriting styles. Towards these problems, a novel self-adaptive cognitive learning framework based on deep learning convolutional neural network (CNN) features is proposed to classify the handwritten document to big five personality traits. This framework correlates the various document level and character level graphology features to big five personality traits to recognize features with strong correlation to various big five personality traits and uses these features to classify the personality. To enhance the deep learning feature learning ability an enhanced convolution kernel is proposed for the CNN. Through testing with various handwritten documents, the proposed solution is found to provide 2.18% higher accuracy and 5% lower false positives compared to existing works on big five personality classification.

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Automated handwriting analysis based on pattern recognition: a survey

Cited by 8 publications

References 45 publications

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Robustness Assessment of Biometric Authenticators

A self adaptive cognitive deep learning framework for classifying graphology features to Big five personality traits

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